Despite many advantages like reduced time of ventilation and hospitalization, early tracheostomy is not associated with decreased mortality in critically ill patients.

Knowledge of these risk factors should increase the anesthesiologist's attention to decide for the necessity to employ prophylactic or therapeutic techniques or drugs to prevent the neonate from any risk resulting of hypotension of the mother.

This study demonstrates that automatical calculation of the NEMS is possible with high accuracy by means of a PDMS. This may lead to a decrease in consumption of nursing resources.

Background It is crucial to rapidly identify sepsis so that adequate treatment may be initiated. Accordingly, the Sequential Organ Failure Assessment (SOFA) and the quick SOFA (qSOFA) scores are used to evaluate intensive care unit (ICU) and non-ICU patients, respectively. As demand for ICU beds rises, the intermediate care unit (IMCU) carries greater importance as a bridge between the ICU and the regular ward. This study aimed to examine the ability of SOFA and qSOFA scores to predict suspected infection and mortality in IMCU patients. Methods Retrospective data analysis included 13,780 surgical patients treated at the IMCU, ICU, or both between January 01, 2012, and September 30, 2018. Patients were screened for suspected infection (i.e., the commencement of broad-spectrum antibiotics) and then evaluated for the SOFA score, qSOFA score, and the 1992 defined systemic inflammatory response syndrome (SIRS) criteria. Results Suspected infection was detected in 1306 (18.3%) of IMCU, 1365 (35.5%) of ICU, and 1734 (62.0%) of IMCU/ICU encounters. Overall, 458 (3.3%) patients died (IMCU 45 [0.6%]; ICU 250 [6.5%]; IMCU/ICU 163 [5.8%]). All investigated scores failed to predict suspected infection independently of the analyzed subgroup. Regarding mortality prediction, the qSOFA score performed sufficiently within the IMCU cohort (AUCROC SIRS 0.72 [0.71–0.72]; SOFA 0.52 [0.51–0.53]; qSOFA 0.82 [0.79–0.84]), while the SOFA score was predictive in patients of the IMCU/ICU cohort (AUCROC SIRS 0.54 [0.53–0.54]; SOFA 0.73 [0.70–0.77]; qSOFA 0.59 [0.58–0.59]). Conclusions None of the assessed scores was sufficiently able to predict suspected infection in surgical ICU or IMCU patients. While the qSOFA score is appropriate for mortality prediction in IMCU patients, SOFA score prediction quality is increased in critically ill patients.

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Summary Objective: Prospective observational study to assess the impact of two differentsampling strategies on the score results of the NEMS, used widely to estimate the amount of nursing workload in an ICU. Methods: NEMS scores of all patients admitted to the surgical ICU over a one-year period were automatically calculated twice a day with a patient data management system for each patient day on ICU using two differentsampling strategies (NEMSindividual: 24-hour intervals starting from the time of admission; NEMS8a.m.: 24-hour intervals starting at 8 a.m.). Results: NEMSindividual and NEMS8a.m. were collected on 3236 patient days; 687 patients were involved. Significantly lower scores were found for the NEMS8a.m. (25.0 ± 8.7) compared to the NEMSindividual (26.1 ± 8.9, p <0.01); the interclass correlation coefficient (ICC) was good but not excellent: 0.78. The inter-rater correlation between the two NEMS scores was high or very high (κ = 0.6-1.0) for six out of nine variables of the NEMS. Conclusions: Different sampling strategies produce different score values, especiallydueto the end of stay. This has to betaken into accountwhen using the NEMS in quality assurance projects and multi-center studies.

Summary Background: Treatment of patients picked up by emergency services can be improved by data transfer ahead of arrival. Care given to emergency patients can be assessed and improved through data analysis. Both goals require electronic data transfer from the emergency medical services (EMS) to the hospital information system. Therefore a generic semantic standard is needed. Objectives: Objective of this paper is to test the suitability of the international nomenclature Logical Observation Identifiers Names and Codes (LOINC) to encode the core data-sets for rescue service protocols (MIND 2 and MIND 3). Encoding diagnosis and medication categories using ICD-10 and ATC were also assessed. Methods: Protocols were broken down into concepts, assigned to categories, translated and manually mapped to LOINC codes. Each protocol was independently encoded by two healthcare professionals and in case of discrepancies a third expert was consulted to reach a consensus. Results: Currently 39% of parameters could be mapped to LOINC. Additional use of other coding systems such as International Statis -tical Classification of Diseases and Related Health Problems (ICD-10) for diagnoses and Anatomical Therapeutic Chemical Classification System (ATC) for medications increases the rate of ‘mappable’ parameters to 56%. Conclusions: Although the coverage is low, mapping has shown that LOINC is suitable to encode concepts of the rescue services. In order to create a generic semantic model to be applied in the field our next step is to request new LOINC codes for the missing concepts.

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